• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.741-750
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• 2011

In this paper, we derive an software mixed debugging model based on a Markov process, assuming that the length of time to perform the debugging is random and its distribution may depend on the fault type causing the failure. We assume that the debugging process starts as soon as a software failure occurs, and either a perfect debugging or an imperfect debugging is performed upon each fault type. One type is caused by a fault that is easily corrected and in this case, the perfect debugging process is performed. An Imperfect debugging process is performed to fix the failure caused by a fault that is difficult to correct. Distribution of the first passage time and working probability of the software system are obtained; in addition, an availability function of a software system which is the probability that the software is in working at a given time, is derived. Numerical examples are provided for illustrative purposes.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.571-577
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• 2012

Modules that consist of software are respectively coded in the early development phase and the modules are unified as a software. After unification, the software is repeatedly tested with a given taskset (the set of module tasks that are tested simultaneously) until a required performance level is satisfied. In this paper, we expand the one-module software debugging model of Jang and Lee (2011) to a multi-module debugging model and derive the taskset completion probability and the mean of the completed tasksets under the assumption that the processing times of module tasks given in a taskset are mutually dependent.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.525-534
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• 2002

Race conditions, races in short, need to be detected for debugging parallel programs, because the races result in unintended non-deterministic executions. To detect the races in an execution of program, previous techniques use a centralized data structure which may incur serious bottleneck in generating concurrency information, or show inefficient time complexity which depends on the degree of nested parallelism in comparing any two of them. We propose a new labeling scheme in this paper, which is scalable in generating the concurrency information without bottleneck by using private data structure, and improves time complexity into constant in checking concurrency. The scalability and time efficiency therfore makes on-the-fly race detection efficient not only for programs with either shared-memory or message-passing, but also for programs with mixed model of the two.